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NASA NTRS2021

VESIcal Part I: An open-source thermodynamic model engine for mixed volatile (H2O-CO2) solubility in silicate melts

Thermodynamics has been fundamental to the interpretation of geologic data and modeling of geologic systems for decades. However, more recent advancements in computational capabilities and a marked increase in researchers’ accessibility to computing tools has outpaced the functionality and extensibility of currently available modeling tools. Here we present VESIcal (Volatile Equilibria and Saturation Identification calculator): the first comprehensive modeling tool for H 2 O, CO 2 , and mixed (H 2 O-CO 2 ) solubility in silicate melts that: a) allows users access to seven commonly used models, plus easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an excel spreadsheet for simple post-modeling analysis; e) integrates advanced plotting capabilities directly within the tool; and f) provides all of these within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python. The tool is presented within this manuscript, which is a Jupyter notebook containing worked examples accessible to python users with a range of skill levels. The basic functions of VESIcal can also be access via a web app (https://vesical.anvil.app). The VESIcal python library is open-source and available for download at https://github.com/kaylai/VESIcal.

K. Iacovino↗

NASA NTRS2020

COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change

Interplanetary exploration occurs at vast distancesthat severely limit communication bandwidth to spacecraft exploringother planets. It is possible to collect much morescientific data than can ever be downlinked given current communicationcapabilities. Therefore, we are developing a systemcalled COSMIC (Content-based Onboard Summarization toMonitor Infrequent Change) that will opportunistically analyzedata onboard a Mars orbiter to alert scientists when meaningfulchanges have occurred. COSMIC will allow future spacecraftto continuously collect data to search for rare, transient phenomenasuch as fresh impacts or seasonally changing polarlandforms under a constrained downlink budget. In this paper,we describe the overall goals and architecture of COSMIC,plans to enable specific scientific studies, label acquisition toenable supervised approaches to surface landform classification,a new machine learning evaluation framework for analyzingthe trade-offs between classifier accuracy and computationalrequirements, and lessons learned about constraints that COSMICwill face operating onboard a spacecraft. In particular, wediscuss design considerations surrounding computational andstorage constraints, change detection strategies, and localizingdetected landforms of interest within a global coordinate frame.Finally, we describe challenges and open research questions thatmust be addressed prior to deploying COSMIC.

Trockman, Asher↗

NASA NTRSDate not supplied

Exploring the Impact of Sideslip Angle on Acoustic Predictions of an Open Rotor System Using Scale-Resolving Simulations

The development of innovative open-rotor propulsion systems that meet performance, fuel efficiency, and noise standards for commercial use depends significantly on the availability of reliable and computationally efficient tools for aerodynamic performance and noise prediction.This investigation demonstrates the capability of the structured curvilinear overset gridComputational Fluid Dynamics solver within the Launch, Ascent, and Vehicle Aerodynamics framework to predict the aerodynamic and aeroacoustic characteristics of a Contra-RotatingOpen Rotor propulsion system under various sideslip angles. In this publication, results obtained using a Hybrid Reynolds-Averaged Navier-Stokes/Large Eddy Simulation-based scale-resolving turbulence closure approach are validated against existing experimental measurements including aerodynamic loads, moments, and tones.

AATT↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗

NASA NTRS2026

Mathematical Model of a Regenerative Fuel Cell for System Optimization

This thesis developed a system-level optimization model of a regenerative fuel cell (RFC) system for long-duration, off-world energy storage applications. Prior RFC design studies have typically been limited to reduced parameter sets and simplified constraints due to computational limitations relative to the number of relevant degrees of freedom. As a result, important nonlinear interactions between subsystems have not been fully captured. This work began to address that gap by developing a higher-fidelity, nonlinear optimization framework that incorporates a broader set of design variables and coupled constraints, enabling a multidimensional model that captures the coupled behavior of RFC subsystems and demonstrates the feasibility of applying optimization to such systems. An expanded system-level optimization approach was established that captures interactions between electrochemical performance, structural requirements, and storage design. This enabled a more comprehensive evaluation of trade-offs than conventional formulations. The model integrates four coupled subsystems: a fuel cell, an electrolyzer, reactant gas, and high-pressure storage tanks, and was formulated to accommodate a wide range of mission parameters, including operational time and required output power. It incorporates constraints on available solar array power, reactant mass balance between production and consumption, and pressure-dependent storage requirements. To enable reliable convergence, the optimization problem was reformulated to reduce dimensionality and improve numerical stability, with subsystem models organized for efficient evaluation. Problem dimensionality was reduced by consolidating lower-level design variables into higher-level representative quantities, and subsystem behavior was evaluated within the optimization loop. A multi-start initialization strategy was employed to mitigate sensitivity to local minima and improve solution quality, while nonlinear relationships were solved using robust numerical methods. The results showed that convergence was achieved across a range of required output power values. Specific energy reached a maximum at a critical mission power level, where the electrolyzer power matched the available solar input and operated near its voltage and current density limits. Beyond this point, further increases in required power resulted in less mass-efficient operation, increasing total system mass and reducing overall performance. The developed model represents an advancement in RFC system-level optimization by enabling analysis of a broader and more tightly coupled design space than previous considerations. While convergence behavior and computational cost remain challenges, the methods introduced improve solvability and allow inclusion of additional design variables with minimal loss of physical fidelity. However, the numerical results should not be interpreted as definitive design recommendations, as the model includes simplifying assumptions and omits several higher-order effects. Future work should extend this framework by incorporating additional subsystems and loss mechanisms, such as thermal management, parasitic power consumption, and reactant losses, to improve fidelity and ensure more representative design conclusions.

Electrochemistry↗

NASA NTRS1989

Computerized structural mechanics for 1990's: Advanced aircraft needs

The needs for computerized structural mechanics (CSM) as seen from the standpoint of the aircraft industry are discussed. These needs are projected into the 1990's with special focus on the new advanced materials. Preliminary design/analysis, research, and detail design/analysis are identified as major areas. The role of local/global analyses in these different areas is discussed. The lessons learned in the past are used as a basis for the design of a CSM framework that could modify and consolidate existing technology and include future developments in a rational and useful way. A philosophy is stated, and a set of analyses needs driven by the emerging advanced composites is enumerated. The roles of NASA, the universities, and the industry are identified. Finally, a set of rational research targets is recommended based on both the new types of computers and the increased complexity the industry faces. Computerized structural mechanics should be more than new methods in structural mechanics and numerical analyses. It should be a set of engineering applications software products that combines innovations in structural mechanics, numerical analysis, data processing, search and display features, and recent hardware advances and is organized in a framework that directly supports the design process.

Viswanathan, A. V.↗

NASA NTRS1992

O-Plan2: The open planning architecture

The O-Plan2 Project at the Artificial Intelligence Applications Institute of the University of Edinburgh is exploring a practical computer based environment to provide for specification, generation, interaction with, and execution of activity plans. O-Plan2 is intended to be a domain-independent general planning and control framework with the ability to embed detailed knowledge of the domain. Various aspects of the project are presented.

Drabble, Brian↗

NASA NTRS1999

Numerical Modeling of Crystal of ZnSe by Physical Vapor Transport - Towards a more Comprehensive Formulations

Crystal growth from the vapor phase has various advantages over melt growth. The main advantage is from a lower processing temperature which makes the process more amenable in instances where the melting temperature of the crystal is high. Other benefits stem from the inherent purification mechanism in the process due to differences in the vapor pressures of the native elements and impurities, and the enhanced interfacial morphological stability during the growth process. Further, the implementation of PVT growth in closed ampoules affords experimental simplicity with minimal needs for complex process control which makes it an ideal candidate for space investigations in systems where gravity tends to have undesirable effects on the growth process. Bulk growth of wide band gap II-VI semiconductors by physical vapor transport has been developed and refined over the past several years at NASA MSFC. Results from a modeling study of PVT crystal growth of ZnSe are reported in this paper. The PVT process is numerically investigated using both two-dimensional and fully three-dimensional formulation of the governing equations and associated boundary conditions. Both the incompressible Boussinesq approximation and the compressible model are tested to determine the influence of gravity on the process and to discern the differences between the two approaches. The influence of a residual gas is included in the models. The results show that both the incompressible and compressible approximations provide comparable results and the presence of a residual gas tends to measurably reduce the mass flux in the system. Detailed flow, thermal and concentration profiles will be provided in the final manuscript along with computed heat and mass transfer rates. Comparisons with the 1-D model will also be provided. The effect of gravity on the process from numerical computations shows subtle effects although experimental evidence from vertically and horizontally grown samples show dramatic evidence of gravitational effects. The shortcomings of the problem formulation will be discussed and a framework will be provided leading up towards a more comprehensive model of PVT systems.

Ramachandran, N.↗

NASA NTRS2001

Requirements engineering and program synthesis: Mutually exclusive or synergistic?

There has often been a clash within the formal methods community between early life-cycle proponents such as the requirements engineering community and late life-cycle proponents such as the program synthesis community. This talk will first characterize these positions and their underlying assumptions, and then expose a common set of problems and approaches. The talk will then propose an integrated life-cycle framework, and expound on its potential benefits. Technical challenges to achieving this integrated life-cycle framework will be described, as well as some preliminary work towards that goal.

Program synthesis↗

NASA NTRS2008

The Super Tuesday Outbreak: Forecast Sensitivities to Single-Moment Microphysics Schemes

Forecast precipitation and radar characteristics are used by operational centers to guide the issuance of advisory products. As operational numerical weather prediction is performed at increasingly finer spatial resolution, convective precipitation traditionally represented by sub-grid scale parameterization schemes is now being determined explicitly through single- or multi-moment bulk water microphysics routines. Gains in forecasting skill are expected through improved simulation of clouds and their microphysical processes. High resolution model grids and advanced parameterizations are now available through steady increases in computer resources. As with any parameterization, their reliability must be measured through performance metrics, with errors noted and targeted for improvement. Furthermore, the use of these schemes within an operational framework requires an understanding of limitations and an estimate of biases so that forecasters and model development teams can be aware of potential errors. The National Severe Storms Laboratory (NSSL) Spring Experiments have produced daily, high resolution forecasts used to evaluate forecast skill among an ensemble with varied physical parameterizations and data assimilation techniques. In this research, high resolution forecasts of the 5-6 February 2008 Super Tuesday Outbreak are replicated using the NSSL configuration in order to evaluate two components of simulated convection on a large domain: sensitivities of quantitative precipitation forecasts to assumptions within a single-moment bulk water microphysics scheme, and to determine if these schemes accurately depict the reflectivity characteristics of well-simulated, organized, cold frontal convection. As radar returns are sensitive to the amount of hydrometeor mass and the distribution of mass among variably sized targets, radar comparisons may guide potential improvements to a single-moment scheme. In addition, object-based verification metrics are evaluated for their utility in gauging model performance and QPF variability.

Molthan, Andrew L.↗

NASA NTRS2012

Prognostics Approach for Power MOSFET Under Thermal-Stress

The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes thermal and power cycling to accelerate the life of the devices. The major failure mechanism for the stress conditions is dieattachment degradation, typical for discrete devices with leadfree solder die attachment. It has been determined that dieattach degradation results in an increase in ON-state resistance due to its dependence on junction temperature. Increasing resistance, thus, can be used as a precursor of failure for the die-attach failure mechanism under thermal stress. A feature based on normalized ON-resistance is computed from in-situ measurements of the electro-thermal response. An Extended Kalman filter is used as a model-based prognostics techniques based on the Bayesian tracking framework. The proposed prognostics technique reports on preliminary work that serves as a case study on the prediction of remaining life of power MOSFETs and builds upon the work presented in [1]. The algorithm considered in this study had been used as prognostics algorithm in different applications and is regarded as suitable candidate for component level prognostics. This work attempts to further the validation of such algorithm by presenting it with real degradation data including measurements from real sensors, which include all the complications (noise, bias, etc.) that are regularly not captured on simulated degradation data. The algorithm is developed and tested on the accelerated aging test timescale. In real world operation, the timescale of the degradation process and therefore the RUL predictions will be considerable larger. It is hypothesized that even though the timescale will be larger, it remains constant through the degradation process and the algorithm and model would still apply under the slower degradation process. By using accelerated aging data with actual device measurements and real sensors (no simulated behavior), we are attempting to assess how such algorithm behaves under realistic conditions.

Power MOSFET PHM↗

NASA NTRS2022

VESIcal: An Open-source Thermodynamic Model Engine for Mixed Volatile (H2O-CO2) Solubility in Silicate Melts

Modeling the solubility of volatiles in silicate melts is fundamental to the interpretation of volcanic systems and has implications for magma dynamics, eruption style, and material transport between the mantle, crust, and atmosphere. Recent advancements in computational capabilities and access to computing tools has outpaced the functionality and extensibility of previously available modeling platforms. Here we present VESIcal (Volatile Equilibria and Saturation Index calculator), the first comprehensive modeling tool for H2O, CO2, and mixed (H2O-CO2) solubility in silicate melts that: a) allows users access to seven popular models, with easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an Excel spreadsheet or CSV file for post-modeling analysis; e) integrates plotting capabilities directly within the tool; and f) provides all of this within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python.Here we will provide a demonstration of VESIcal and its capabilities with applications to various volcanic processes affected by volatiles. VESIcal represents the first tool capable of directly comparing multiple solubility models and equations of state. We find that commonly used models predict surprisingly different volatile solubilities, particularly for pure CO2 or mixed CO2-H2O fluids. Even for melt compositions that are well represented in the calibration datasets of multiple models (e.g., MORBs), calculated solubilities for pure CO2 and pure H2O can deviate from one another by factors of >2 leading to 2x deviations in calculated saturation pressures (e.g., 5 to 10 kbar). The solubility of CO2 predicted by different rhyolitic models also differs substantially, overwhelming other sources of uncertainty such as analytical errors on measurements of volatile contents or uncertainties in crustal density profiles. This highlights the importance of model choice when drawing geological conclusions based on volatiles in magmas.

Kayla Iacovino↗

NASA NTRS1986

Rationalizing VICAR within a TAE framework: Some problems and some solutions

Transportable Applications Executive (TAE) implementation may impose a strain on centers with modest resources. This may be eased in a number of ways. The balance of a small number of expert users and a large number of computing novices at the Interactive Planetary Image-Processing System (IPIPS) imposes special constraints. Some solutions to these and other particular problems are described.

Harris, Colin P.↗

NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heating condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating condition is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty in reconstructed Mars 2020 backshell heating for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of the reconstruction uncertainty. These results demonstrate Green’s functions and sensor-fusion techniques as promising IHT approaches to reconstruct atmospheric entry environments from TPS-embedded measurements, and highlight how these techniques may give access to post-flight analyses previously hindered by the prohibitive cost of current methods.

Kenneth McAfee↗

NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Kenneth McAfee↗

NASA NTRS1986

A study of pipelining in computing arrays

Scheduling considerations in computing arrays are examined. A simple sufficient condition is developed for determining whether a computing array can be pipelined. If the array cannot be pipelined in the form given, the condition also indicates the direction in which to proceed to make it pipelineable. The overall framework and methodology take a good part of the load off the logical architect of the array, and make the translation from the logical to the physical architecture a mechanical process.

Jagadish, H. V.↗

NASA NTRS1989

An embedded grid formulation applied to delta wings

An embedded grid algorithm for the Euler and/or Navier-Stokes equations is developed and applied to delta wings at high angles of attack in low speed flow. The Navier-Stokes code is an implicit, finite volume algorithm, using flux difference splitting for the convective and pressure terms and central differencing for the viscous and heat transfer terms. Calculations are compared with detailed experimental results over an angle of attack range up to and beyond the maximum lift coefficient, corresponding to vortex breakdown at the trailing edge, for a delta wing nominally of unit aspect ratio. The results indicate that the overall flowfield, including surface pressures, surface streamlines, and vortex trajectories, can be simulated accurately with the global grid version of the present algorithm. However, comparison of computed velocities and vorticity with experimentally measured off-body values at an angle of attack of 20.5 deg indicates the core region is substantially more diffuse in the computations than that measured with either a five-hole probe or a laser velocimeter. Embedded grids, used to improve the numerical discretization in the core region, are formulated within the framework of the implicit, upwind-biased multi-grid algorithm. Structured levels of local nested refinements are made. Three-dimensional results for both Euler and Navier-Stokes calculations are shown, with up to 3 levels of embedded refinement. The embedding procedure was effective in eliminating a crossflow secondary separation produced in the Euler solutions on coarse grids.

Thomas, James L.↗

NASA NTRS1999

A Simple Tool for the Design and Analysis of Multiple-reflector Antennas in a Multi-disciplinary Environment

The process of designing and analyzing a multiple-reflector system has traditionally been time-intensive, requiring large amounts of both computational and human time. At many frequencies, a discrete approximation of the radiation integral may be used to model the system. The code which implements this physical optics (PO) algorithm was developed at the Jet Propulsion Laboratory. It analyzes systems of antennas in pairs, and for each pair, the analysis can be computationally time-consuming. Additionally, the antennas must be described using a local coordinate system for each antenna, which makes it difficult to integrate the design into a multi-disciplinary framework in which there is traditionally one global coordinate system, even before considering deforming the antenna as prescribed by external structural and/or thermal factors. Finally, setting up the code to correctly analyze all the antenna pairs in the system can take a fair amount of time, and introduces possible human error.

Katz, Daniel, S.↗